It is well known to capture image data using digital image sensors. An array of light sensitive picture elements (pixels) is provided, which can be manufactured as charge coupled devices or using complementary metal oxide semiconductor (CMOS) techniques. Incident radiation causes a charge to be generated at each pixel. This charge is converted to a voltage whose value is then digitised. The value of the voltage depends upon the intensity of the illumination incident on the pixel during an integration time, when the pixels are set in a light sensitive mode. A pixel array can be formed in one, two or three dimensions. The most common form is a two dimensional pixel array as found in everyday cameras for example. A one dimensional pixel array is typically referred to as a “linear array”, and an image sensor with such an array can be termed a linear sensor or a linear scanner.
The set of intensity values derived from the pixel array is known as image data. The “raw” image data output by the pixel array may be subjected to various post-processing techniques in order to reproduce an image either for viewing by a person or for processing by a machine. These post-processing techniques can include various statistical methods for image analysis and for performing various camera and image processing functions.
One example of such techniques is the recognition and/or classification of textures within an image. The texture can represent surface characteristics of an object or region, and can be used as a basis for identifying different objects or different regions of an object within an image. Modelling texture is usually characterised by variations in signal intensity, and sometimes the spatial relationship (local neighbourhood properties) of these variations in an image.
It is know to use two dimensional wavelet transforms in a method for modelling texture. Wavelet transforms are useful because they give the ability to construct a time-frequency representation of a signal that offers very good time and frequency localisation.
An introduction to wavelets can be found from US 2010/0014761 and is provided below in the detailed description section.
However these methods are not tolerant to fragmentation of the textural features being measured, that is, when the textural features comprise diffuse, irregular, broken or spaced patterns in the image. There are also limits to the resolution and resolving power of existing techniques.
There is therefore a need for a method that is more robust to fragmentation in the textural images, and/or that has improved resolution, and/or that has an improved resolving power with respect to existing wavelet based techniques.